Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm

Rani, R. Ranjani; Ramyachitra, D.

doi:10.1016/j.biosystems.2016.10.005

Cited by 26 publications

(14 citation statements)

References 43 publications

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“…In the last few years, the BFO algorithm has been successfully applied to solve real-world engineering optimization problems [38]. It has been pointed out that the BFO algorithm can reduce the global convergence, computational burden, time and also handle a number of objective functions [39]. Comparisons with the conventional intelligent evolutionary computation algorithms, such as the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have shown that BFO can realize a better optimization performance in many practical problems and real applications [40][41][42].…”

Section: Bfo Algorithmmentioning

confidence: 99%

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu

Cai

et al. 2020

Energies

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Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency.

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Section: Bfo Algorithmmentioning

confidence: 99%

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Liu

Cai

et al. 2020

Energies

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“…Ranjani Rani, Dr. D. Ramyachitra [19]proposed a hybrid algorithm called Multi-Objective Bacterial Foraging Optimization Algorithm (MO-BFO) for solving Multiple sequence alignment problems. The proposed work consists of two algorithms, the first algorithm is Hybrid Genetic Algorithm with Artificial Bee Colony (GA-ABC), while the second is the Bacterial Foraging Optimization Algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…the disadvantage of this algorithm is GPU-based and cannot be employed by CPUbased computers. The primary drawbacks of the previous related works [19][20] [21] and the others that introduced in the Introduction are they require more memory allocation and time-consuming. While the proposed work requires less memory size and CPU computing time due to reducing the alignment search space at matching Region process and due to the paralleling of the alignment space with the Genetic algorithm.…”

Section: Related Workmentioning

confidence: 99%

Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm

Abedulridha¹,

Al-Shamery²

2018

IJET

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DNA sequence alignment is an important and challenging task in Bioinformatics, which is used for finding the optimal arrangement between two sequences. In this paper, two methods are proposed in two stages to solve the pairwise sequence alignment problem. The first method is Matching Regions(MR) concerns on splitting the DNA into regions with adaptive interleaving windows to isolate the DNA tape into matched and non-matched regions. Additionally, a Multi-Zone Genetic Algorithm (MZGA) is proposed as an improved method in the second stage. It consists of segmenting a large non-matched region into smaller search space. Then, the MZGA is implemented in parallel to save time. Genetic Algorithm can be applied as an optimization toolto produce multiple solutions. Furthermore, the improvement focuses on the enhancement of Simple GA operators. In the selection, the population is divided into three Zones according to the fitness score. A new crossover approach is proposed depending on cut-points and location of gaps. The proposed method guarantees that the value of fitness tends to improvement or convergence in each successive generation. Thus, the offs pring of populations will have better fitness value. The system has been applied to the real-world dataset of DNA with variable lengths which are ranged from 66 bases up to 26037 bases. As a result, the proposed technique satisfied the best alignment score of the DNA sequences. Finally, it is worth mentioning that the proposed system proved to be generalizable.

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“…Finally, Rani et al proposed in [21] two algorithms, the Hybrid Genetic Algorithm with Artificial Bee Colony Algorithm (GA-ABC) and the Bacterial Foraging Optimization Algorithm (MO-BFO), but in their work they focused mainly on MO-BFO algorithm because it performed better and it also identified conserved blocks. They incorporate in their work four objectives to optimize: maximization of similarity, non-gap percentage, conserved blocks, and minimization of gap penalty.…”

Section: Related Workmentioning

confidence: 99%

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

et al. 2017

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Multiple sequence alignment (MSA) is an optimization problem consisting in finding the best alignment of more than two biological sequences according to a number of scores or objectives. In this paper, we consider a threeobjective formulation of MSA, which includes the STRIKE score, the percentage of aligned columns, and the percentage of non-gap symbols. The two last objectives introduce many plateaus in the search space, thus increasing the complexity of the problem. By taking as benchmark the BAliBASE data set, we carry out a rigorous comparative study by using four multi-objective metaheuristics, including the classical NSGA-II evolutionary algorithm and the more recent ones

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Multiple sequence alignment using multi-objective based bacterial foraging optimization algorithm

Cited by 26 publications

References 43 publications

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Boundary Identification for Traction Energy Conservation Capability of Urban Rail Timetables: A Case Study of the Beijing Batong Line

Optimal Pair DNA Sequence Alignment based on Matching Regions and Multi-Zone Genetic Algorithm

Comparing multi-objective metaheuristics for solving a three-objective formulation of multiple sequence alignment

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